Blank Bio, an applied AI research lab focused on training foundation models for RNA, announced the closing of a $7.2 million seed financing round along with a strategic collaboration with Pacific Biosciences to advance RNA foundation models for precision oncology.
The company is developing RNA foundation models that learn directly from the molecular complexity of tumor transcriptomes to improve patient-level prediction across oncology. The funding proceeds will support continued model development, expanded collaborations with pharmaceutical and diagnostic companies, and the generation of new long-read RNA sequencing datasets for applications in biomarkers, clinical trial design, and diagnostics.
As part of the collaboration, Blank Bio will generate PacBio HiFi long-read bulk RNA sequencing data from up to 100 fresh frozen patient tumor samples across multiple cancer indications. The sequencing work will be conducted at Seattle Children’s Research Institute, where Kinnex RNA libraries will be automated using the SPTLabtech firefly+ platform.
Blank Bio said the data will be used to further train and evaluate its models, particularly in oncology applications where RNA-level signals may improve patient stratification, biomarker discovery, and clinical interpretation.
The company noted that standard analytical workflows often compress RNA-seq data into per-gene count summaries, which can limit the ability to capture isoform architecture, mutational complexity, and other patient-specific tumor biology features. Blank Bio aims to use foundation models to preserve and interpret more of the biological signal contained in transcriptomic data.
The oversubscribed seed round included participation from Define Ventures, Leonis Capital, Nova Threshold, Ripple Ventures, SignalFire, Y Combinator, and others.
Blank Bio said it is deploying its RNA foundation models across predictive biomarkers, prognostic biomarkers, patient trajectory modeling, and clinical diagnostics. The company is also working with diagnostic companies to augment existing RNA-seq tests and improve diagnostic sensitivity and specificity.
The team includes AI scientists and engineers from organizations including Recursion, Deep Genomics, DeepMind, and Amazon, as well as researchers affiliated with Memorial Sloan Kettering Cancer Center, Stanford University, and the Vector Institute.
KEY QUOTES:
“Bulk RNA-seq is one of the most clinically accessible and information-rich assays in oncology, but much of its signal is still reduced to simplified gene-level summaries. Blank Bio was founded to apply foundation models to the full molecular detail contained in each patient’s tumor transcriptome and turn that information into more precise, clinically useful predictions. This financing will support continued model development and partnership expansion, while our collaboration with PacBio will generate the high-resolution long-read RNA data needed to further train and evaluate these models in patient tumor samples.”
Jonathan Hsu, CEO and Co-Founder, Blank Bio
“PacBio HiFi long-read sequencing was built to resolve biology that other technologies miss, and nowhere is that more consequential than in the complex transcriptomes of patient tumors. Blank Bio’s foundation models demonstrate how high-resolution RNA data and machine learning can advance the next generation of precision oncology applications, from biomarkers and diagnostics to clinical trial design.”
David Miller, Global Vice President Of Marketing, PacBio
“Blank Bio is building at the intersection of two major shifts in biology: the expanding clinical use of RNA-seq and the emergence of foundation models capable of learning complex biological patterns at scale. The company brings together deep scientific and technical expertise in RNA biology, machine learning, and oncology, with a platform that has the potential to turn transcriptomic data into a more powerful layer of patient-level insight for drug development and diagnostics.”
Sahir Raoof, TechBio Advisor, SignalFire
